Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG

• Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Physiology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinsk bioteknologi -- Biomedicinsk laboratorievetenskap/teknologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Medical Biotechnology -- Biomedical Laboratory Science/Technology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk laboratorie- och mätteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Laboratory and Measurements Technologies (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering (hsv//eng)

Keyword

EEG

classification

burst

suppression

neonatal

neonatal care

Medicinteknik

EEG

classification

Burst

Suppression

Neonatal

Publication and Content Type

ref (subject category)

kon (subject category)